Measuring Innovation

Jonas Kreutzer

2023-09-08

Subject Oriented Measurements

Surveys

Example: European Community Innovation Survey

Three Main Uses of Surveys

  1. Descriptive overviews

  2. Studies for policy analysis

  3. Econometric / statistical analysis

Surveys summary

Flexible

Rich

Response rate

Delimination of innovation expenditure

Subjectivity of novelty

R & D

Research and experimental development (R&D) comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.

OECD (1994)

Types of R & D

Basic Research is experimental or theoretical work undertaken primarily to acquire new knowledge about observable phenomena and facts, not directed toward any particular use.

Applied Research is original investigation to acquire new knowledge directed primarily towards a specific practical aim or objective.

Experimental Development is systematic effort, based on existing knowledge from research or practical experience, directed toward creating novel or improved materials, products, devices, processes, systems, or services.

R & D Summary

Long Time Series

Decomposable (Type of Research, Org)

Available at Firm Level

Not necessarily innovation

Not the only innovation input

Biased against small firms

Biased against service / organizational innovation

Measurement error due to false allocation of spending

Object Oriented Measurements

Patents

Patent Office Interfaces

OECD patstat: https://www.epo.org/searching-for-patents/business/patstat.html

USPTO: https://patentsview.org/

European Patent Office: https://worldwide.espacenet.com/

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Patents summary

Long time series

Accessible

Detailed

Differing propensity to patent

Patenting motivation

Possible Industry-Technology mismatch

Multiple patent offices (EPO; USPTO; JPO)

LBIO

Article example

Example of an Innovation Article

Source: “Svensk Trävaru- och Pappersmassetidning 9 - 1985

LBIO summary

High data quality

Captures actual innovation

Reliable coverage due to expert filter

Potentially biased against process innovations

Biased against incremental innovation

Labor intensive to produce

References

Johansson, M., Nyqvist, J., & Taalbi, J. (2022). Linking innovations and patents - a machine learning assisted method [{{SSRN Scholarly Paper}}]. https://doi.org/10.2139/ssrn.4127194
OECD. (1994). The Measurement of Scientific and Technical Activities: Standard Practice for Surveys of Research and Experimental Development - Frascati Manual 1993. Organisation for Economic Co-operation and Development.
Rammer, C., & Es-Sadki, N. (2022). Using Big Data for Generating Firm-Level Innovation Indicators A Literature Review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4072590